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The field of the invention is gated computerized tomography (CT) imaging and more specifically methods and apparatus that reduce the amount of misalignment between parallel two dimensional images in a working CT image set.
Many different types of medical imaging systems have been developed that are used for different purposes. Perhaps the most common type of imaging system category includes X-ray systems wherein radiation is directed across a portion of a patient to be imaged and toward a detector panel. An exemplary X-ray detector panel includes a CsI scintillator coupled with an amorphous silicon array. With radiation directed toward a region of a patient to be images (i.e., a region of interest), the region of interest blocks some of the radiation and some of the radiation passes through the region and is collected by the panel. The amount of radiation that passes through the region along the trajectory of a given radiation ray depends upon the type of tissue along the trajectory. Thus, a tumor may block more radiation than flesh and bone may block more radiation than a tumor and so on. Hence X-ray system can be used to collect a xe2x80x9cprojectionxe2x80x9d through a patient.
While useful, simple X-ray systems have many limitations. One important limitation to X-ray imaging systems is that such systems, as described above, only provide side projections through a region and cannot be used to generate other useful images such as xe2x80x9cslicexe2x80x9d images (i.e., images perpendicular to projection images) through a region of interest. For instance, an exemplary useful slice image may include a slice image through a patient""s heart.
Another type of imaging system that is useful in generating slice images is generally referred to as a computerized tomography (CT) system. An exemplary CT system includes a radiation source and a radiation detector mounted on opposite sides of an imaging area where the imaging area is centered along a translation or Z-axis. The source generates radiation that is collimated into a beam including a plurality of radiation rays directed along trajectories generally across the imaging area. A line detector may be positioned perpendicular to the Z-axis to collect slice image data during a data acquisition period.
During an acquisition period a region of interest is positioned within the imaging area and, with the radiation source turned on, the region of interest blocks some of the radiation and some of the radiation passes through the region and is collected by the line detector. As in X-ray systems, the amount of radiation that passes through the region of interest along the trajectory of a given radiation ray is dependent upon the type of tissue along the trajectory. In CT systems the source and line detector are rotated about the region of interest within a rotation plane through the region of interest so that radiation xe2x80x9cprojectionsxe2x80x9d can be collected for a large number of angles about the region. By combining the projections corresponding to a slice through the region of interest using a filtering and back projecting technique, a two-dimensional tomographic or axial image (i.e., a slice image) of the slice is generated.
While some diagnostic techniques only require one or a small number of slice images, many techniques require a large number of parallel CT slice images. For example, some techniques require examination of many parallel images to identify where an arterial blockage begins and ends and the nature of the blockage there between. As another example, many techniques reformat two dimensional data into, in effect, three dimensional volumetric images, that can be sliced and diced in several different directions so that various image planes can be employed. For instance, where two dimensional data is acquired for transverse or cross sectional slices through a three dimensional region of interest (e.g., through a patient""s torso), the data may be reformatted to generate sagital (i.e., the side plane passing through the long axis of the body) or coronal (i.e., the frontal plane passing through the long axis of the body) images through the region of interest.
In order to generate several slice images rapidly, CT detectors are typically configured having several parallel detector rows such that, during a single rotation about the imaging area, each detector row collects data that can subsequently be used to generate a separate CT slice image.
While increasing the number of detector rows reduces acquisition time, detector elements are relatively expensive and thus more rows translates into a more costly overall system. As a balance between cost and speed, most multi-row detectors include less than 10 detector rows. Hereinafter it will be assumed that an exemplary detector includes eight detector rows.
Where a detector includes eight rows and more than eight slice images are required, several different acquisition periods are typically used to acquire the necessary slice image data. For instance, assume that 80 slice images (an admittedly small number but sufficient for exemplary purposes) through a ROI are required. In this case, the ROI may be divided into ten separate sub-volumes, each of the ten sub-volumes corresponding to a separate eight of the 80 required slice images. Thereafter, ten separate acquisition periods may be used to collect the sets of slice image data corresponding to the ten sub-volumes, data corresponding to eight separate slice images collected during each of the ten separate acquisition periods.
It has been found that, for large volumes or ROIs, data necessary to generate many parallel thin slice images can be acquired most rapidly by helically collecting the data. To this end, while the source and detector are rotated about the imaging area, a patient bed is translated there through so that the radiation fan beam sweeps a helical path through the ROI. After helical data is collected, the data is converted to slice image data by any of several different weighting and filtering processes and thereafter the slice image data is backprojected to form the viewable image.
In the case of helically acquired and stored raw data, the data can be used to construct virtually any number of slice images through a corresponding ROI. For instance, despite using a detector having eight rows of elements to collect helical data, the data may be processed to generate 16, 20, 500 or even thousands of separate slice images or, indeed, may be interpolated to generate a 3-D volumetric image, if desired.
In most imaging systems that generate still images, it is important that, to the extent possible, during data acquisition, the structure being imaged remain completely still. Even slight structure movement during acquisition can cause image artifacts in, and substantially reduce the diagnostic value of, resulting images. For this reason, during acquisition periods, patients are typically instructed to maintain the region of interest within the imaging area as still as possible by, for instance, holding the patient""s breadth.
Despite a patient""s attempts to control movement, certain anatomical structures cannot be held still and continue movement during acquisition periods. For instance, a patient""s heart beats continually during data acquisition cycles and the beating movement complicates the process of acquiring diagnostic quality data.
In the case of the heart, fortunately, the beating cycle is repetitive and there are certain cycle phases during which the heart muscle is relatively at rest. As well known in the art, during a diastolic phase of the beating cycle when the heart is filling with blood, the heart is relatively at rest and movement is minimal. Thus, by restricting data acquisition periods to the diastolic phases of the heart beating cycle, relatively movement-free data can be acquired and used to generate CT slice images.
To this end, the industry has developed cardiac gated CT imaging systems. These systems generally take two different forms including shoot and move gating scans and retro-gating reconstructions. In the case of shoot and move scans, an electrocardiogram (EKG) system is used to monitor heart beating phase and to gate the acquisition of data so that data is only acquired during specific phases of the heart beating cycle (e.g., systolic, diastolic, etc.) Thereafter the data is used to generate slice images in a conventional manner. In the case of retro-gating reconstruction, a full set of helical data is acquired and stored along with corresponding EKG signals. Thereafter, a heart cycle phase range is selected which indicates a range of the cycle for which images should be generated and an image reconstructor retrieves the helical data sub-set corresponding to the phase range from each heart cycle and generates the required images.
In addition to minimizing movement related image artifacts, each of the gating processes (i.e., prospective and retrospective) is also meant to reduce misregistration between sets of images that are generated using data corresponding to different sub-volumes of a region of interest. For instance, in the case above where a region is divided into ten separate sub-volumes and data for each sub-volume is collected during a separate acquisition period, if data for two consecutive sub-volumes were collected during different heart beating phase, resulting images would likely be misaligned. Thus, by collecting data for all sub-volumes during similar heart beating phases, misalignment is substantially reduced. In the cases of axially acquired data and helically acquired data this means restricting data to a specified phase range within each heart beating cycle. For instance, the acquired period may be between 70% and 80% of the total heart beating cycle where the cycle begins and ends at peak cycle amplitudes.
Hereinafter the phrase xe2x80x9cphase locationxe2x80x9d will be used to refer to as a phase point within a heart cycle and the phrase xe2x80x9cphase rangexe2x80x9d will be used to refer to a range that is centered on a corresponding phase location.
Unfortunately, despite cardiac gating efforts, it has been recognized that mis-registration or misalignment of sub-volume images can still occur for several reasons. First, as known in the industry, EKG signals provide only an indirect way to measure heart motion and therefore cannot be used to precisely identify identical phase locations within a heart beating cycle. Second, it is known that, while generally periodic, the heart muscle may not go through precisely the same motions during consecutive heart cycles so that, even if precise phase locations could be identified within a heart beating cycle, those locations may not correspond to a similarly positioned heart. Third, in the case of high heart rates (i.e., a child""s heart) the gating system may have inadequate temporal resolution to facilitate proper gating. These gating problems are further exacerbated when attempting to generate images including coronary arteries as segments of a given artery may be at rest at somewhat different phase locations within consecutive heart beating cycles.
Gating related phase mis-registrations can be quite apparent in ventricle walls when viewing an image dataset corresponding to a multi-planar reformat rendering from a sagital or coronal perspective. Similarly, the mis-registrations are apparent in the coronary arteries when viewing an image dataset with a curved reformat rendering.
It has been recognized that when gated CT techniques are employed to collect image data corresponding to adjacent ROI sub-volumes during sequential acquisition phases, where each acquisition phase is further sub-divided into shorter phase ranges, often image sets corresponding to the different phase ranges within sequential acquisition phase align better than image sets corresponding to the same phase ranges within sequential acquisition phases. For example, assuming adjacent first and second sub-volumes corresponding to first and second sets of eight slice images and that data corresponding to the first and second sub-volumes is to be collected during the diastolic phases of first and second heart beating cycles, respectively. In this case, each of the first and second diastolic phases may be divided into beginning and ending phase ranges including first and second halves of the first and second diastolic phases, respectively.
Also, assume that during the first diastolic phase two sets of image data for the first sub-volume (i.e., for the eight slices of the first sub-volume) are obtained, a first set obtained during the beginning phase range and a second set obtained during the ending phase range. Similarly, assume that during the second diastolic phase two sets of image data for the second sub-volume (i.e., for the seven slices of the second sub-volume) are obtained, a first set obtained during the beginning phase range and a second set obtained during the ending phase range. Even though the beginning phase range data sets correspond to similar phase ranges of the heart beating cycle, it has been found that images generated using the beginning phase range data sets are often characterized by greater mis-registration than images generated using the beginning phase range data set from the first diastolic phase and the ending phase range data set from the second diastolic phase.
Thus, it has been recognized that where data is collected for each sub-volume of a ROI during separate diastolic phases, instead of obtaining a single data set corresponding to each diastolic phase, the diastolic phase can be divided into several phase ranges and a separate sub-volume data set can be obtained for each phase range and thereafter, during post acquisition processing, image sets corresponding to the different phase ranges can be compared and the sets that align or register most accurately can be combined into a working image set for further diagnostic purposes. While the example above where each diastolic phase is divided into beginning and ending phase ranges facilitates better results than systems that do not divide the diastolic phase, alignment is generally further enhanced as the number of diastolic phase divisions are increased. For instance, generally, dividing each diastolic phase into five phases ranges yields better results than dividing each diastolic phase into two phase ranges.
Throughout this specification, while some of the examples are described in the context of either a prospective gating method or a retrospective gating method, it should be understood that the present invention is useable with in prospective or retrospective methods and processors and should not be limited to one or the other. It should suffice to say that where an example is taught in the context of one or the other types of systems, the type of system not literally taught has only been omitted in the interest of simplifying this explanation and not to limit the invention in any way.
It should also be noted that the present invention is also useful in the case of advanced multi-sector reconstruction algorithms to improve temporal imaging resolution. These algorithms and how the present invention would be used therewith should be obvious to one of ordinary skill in the art in light of the specification which follows.
These and other aspects of the invention will become apparent from the following description. In the description, reference is made to the accompanying drawings which form a part hereof, and in which there is shown a preferred embodiment of the invention. Such embodiment does not necessarily represent the full scope of the invention and reference is made therefore, to the claims herein for interpreting the scope of the invention.